R: The Swiss Army Knife of Science

• The R project for statistical computing is a free open source statistical programming language and project. Follow these steps to get started.
• The main advantages of R are the fact that R is freeware and that there is a lot of help available online.

Why use R?

• Strengths
  • free and open source, supported by a strong user community
  • highly extensible and flexible
  • implementation of modern statistical methods
  • assists with replication and extension of your work
• Weaknesses
  • slow with very large data sets (>1GB)
  • non-standard programming paradigms

Why use R?

• RStudio is the primary Integrated Develop Environment (IDE) for R
• We will be using RStudio for the tutorial

RStudio IDE

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Packages/Libraries

• Packages are the main R tools
• There are 15388 packages. It’s probable that if you have thought about the analysis, there is a package that is able to do it already. There are two basic steps to using a package:

Installing the package install.packages("tidyverse")

Loading the package library(tidyverse)

Packages/Libraries

• Packages include functions that do things.

data <- read_csv(“/The/Path/To/Your/File.csv”)

• data = where you want to save the data
• <- = tell the function where to put the output
• read_csv = the name of the function
• () = what you input into the function

Doing simple things in R

## [1] 4

## [1] 8

Reading in some data

The great R wars

Two similar but different methods for many common tasks

• Tidyverse
• Base R

Where to find packages

• CRAN Task View
• META Cran
• Follow prominent voices
  • JennyBryan
  • Hadley Wickham
  • Amelia MN

Ocean Plastic Sciences

• marelac
• oce

Political Sciences

• http://m-clark.github.io/docs/RSocialScience.pdf
• Twitter Sentiment Analysis
  • https://dataaspirant.com/2018/03/22/twitter-sentiment-analysis-using-r/

Workflow Revolution

• RStudio + Github
  • https://github.com/TeamINTERACT/onboarding
• Full Scientific Paper with R and RMarkdown
  • Example1
  • Example2